Using Vision Language Models to extract demographic signals from Sentinel-2 satellite imagery — and testing whether machines can see what surveys measure.
India's demographic surveys are rich but infrequent and expensive. Can satellite imagery — analysed by Vision Language Models — provide a continuous, scalable proxy for demographic quality? This study investigates four questions that sit at the intersection of earth observation, large language models, and development policy.
The pipeline integrates three AI systems — Google Earth Engine for cloud computing over petabytes of satellite imagery, the Copernicus Sentinel-2 constellation for 10-metre resolution optical data, and Anthropic's Claude Vision API for structured feature extraction and narrative synthesis.
True-colour composites (Bands B4, B3, B2) for all 12 districts. Each image covers a 10km × 10km window centred on the district headquarters. 2023 cloud-free annual composite. Rows represent clusters; columns are individual districts.